Previous datasets for overhead imagery focus on classification [1]}, bounding box object detection [2]}, [3]}, [4]}, instance-based segmentation [5]}, and object tracking [6]} tasks. None of these datasets comprise multiple images of the same field of view from substantially different look angles, making it difficult to assess model robustness to new views. Within segmentation datasets, SpaceNet [5]} represents the closest work, with dense building and road annotations created by the same methodology. We summarize the key characteristics of each dataset in Table REF . Our dataset matches or exceeds existing datasets in terms of imagery size and annotation density, but critically includes varying look direction and angle to better reflect the visual heterogeneity of real-world imagery.
